Aiming at mitigating end effects of empirical mode decomposition （EMD）, a new approach motivated by the non- equidistance grey model （NGM） termed as NGM（1,1） is proposed. Other than trapezoid formulas, the cubic...Aiming at mitigating end effects of empirical mode decomposition （EMD）, a new approach motivated by the non- equidistance grey model （NGM） termed as NGM（1,1） is proposed. Other than trapezoid formulas, the cubic Hermite spline is put forward to improve the accuracy of derivative to the accumulated generating operation （AGO） series. Hopefully, it is worth stressing that the proposed NGM（1,1） model is particularly useful for predicting uncertainty data. Qualitative and quantitative comparisons between the proposed approach and other well-known algorithms are carried out through computer simulations on synthetic as well as natural signals. Simulation results demonstrate the proposed method can reduce end effects and improve the decomposition results of EMD.展开更多
经验模分解(Em p iricalM ode D ecom position,EMD)是希尔伯特-黄变换(HHT)的核心,而经验模分解方法的关键是对提取固有模式函数(Intrinsic m ode function,IM F)时所谓边缘效应问题的处理。提出了极值点对称延拓方法,用来对边缘效应...经验模分解(Em p iricalM ode D ecom position,EMD)是希尔伯特-黄变换(HHT)的核心,而经验模分解方法的关键是对提取固有模式函数(Intrinsic m ode function,IM F)时所谓边缘效应问题的处理。提出了极值点对称延拓方法,用来对边缘效应问题进行处理。算例分析结果表明该方法的算法简单,计算速度快,能有效地抑制EMD分解时的边缘效应,分解得到的固有模式函数完备地体现了原信号真实的频率和幅值信息。在信号重构时不会带来原始信号的畸变。展开更多
基金supported by the National Natural Science Foundation of China (60975009 61171197+6 种基金 61174016)the Innovative Team Program of the NNSF of China (61021002)the National Basic Research Program of China (973 Program) (2012CB720000)the Shandong Provincial Natural Science Foundation (ZR2011FM005)the Promotive Research Fund for Excellent Young and Middle-aged Scientists of Shandong Province (BS2010DX001)the Research Fund for the Doctoral Program of Higher Education of China (20092302110037 20102302110033)
文摘Aiming at mitigating end effects of empirical mode decomposition （EMD）, a new approach motivated by the non- equidistance grey model （NGM） termed as NGM（1,1） is proposed. Other than trapezoid formulas, the cubic Hermite spline is put forward to improve the accuracy of derivative to the accumulated generating operation （AGO） series. Hopefully, it is worth stressing that the proposed NGM（1,1） model is particularly useful for predicting uncertainty data. Qualitative and quantitative comparisons between the proposed approach and other well-known algorithms are carried out through computer simulations on synthetic as well as natural signals. Simulation results demonstrate the proposed method can reduce end effects and improve the decomposition results of EMD.
文摘经验模分解(Em p iricalM ode D ecom position,EMD)是希尔伯特-黄变换(HHT)的核心,而经验模分解方法的关键是对提取固有模式函数(Intrinsic m ode function,IM F)时所谓边缘效应问题的处理。提出了极值点对称延拓方法,用来对边缘效应问题进行处理。算例分析结果表明该方法的算法简单,计算速度快,能有效地抑制EMD分解时的边缘效应,分解得到的固有模式函数完备地体现了原信号真实的频率和幅值信息。在信号重构时不会带来原始信号的畸变。
